For the WB experiments, we use HGCN (CEM+GHCM+HM) as the reference and introduce the improvements of each module step by step for comparison (\(\text{CEM}^+\) , \(\text{GHCM}^+\) , \(\text{HM}^+\) , \(^+\)  denotes the updated version). The 32 ms hanning window with \(25\%\)  overlap and 512-point STFT is used. The kernel size is \((5,2)\) . The channel number and stride of encoder and decoder are \(\left\lbrace 12,24,48,64,96,96\right\rbrace \)  and \((2,1)\) . For HGCN, a 512-units FC after 3-layer 128-units LSTM is adopted. The channel number and stride of GHCM are \(\left\lbrace 8,16,8\right\rbrace \)  and \((1,1)\)  and the out channel of last decoder is 22 (\(\text{C}_\text{A}=\text{C}_\text{B}=10\) ). Since the \(\text{GHCM}^+\)  and the DPRNN are residual, the hidden dimension is the same as the input. The optimizer is Adam [1]}. And the initial learning rate is \(0.001\) , which decays \(50\%\)  when the valid loss plateaus for 5 epochs, and training is stopped if loss plateaus for 20 epochs.
